Integrating Local and Global Error Statistics for Multi-Scale RBF Network Training: An Assessment on Remote Sensing Data

• Published in: PloS one Vol. 7; no. 8; p. e40093
• Main Authors: Mountrakis, Giorgos, Zhuang, Wei
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 02.08.2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Architectural engineering; Artificial neural networks; Back propagation networks; Basis functions; Biology; Classification; Computer Science; Computer simulation; Consistency; Data acquisition; Data mining; Data processing; Datasets; Detection; Earth Sciences; Ecosystems; Engineering; Environmental science; Error detection; Humans; Image classification; Image detection; Landsat; Landsat satellites; Lasers; Learning algorithms; Lidar; Machine learning; Mathematics; Methods; Models, Statistical; Multiscale analysis; Neural networks; Neural Networks (Computer); Optical radar; Radial basis function; Regression; Remote sensing; Satellite imagery; Satellites; Social and Behavioral Sciences; Statistics; Training; New York; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0040093

This study discusses the theoretical underpinnings of a novel multi-scale radial basis function (MSRBF) neural network along with its application to classification and regression tasks in remote sensing. The novelty of the proposed MSRBF network relies on the integration of both local and global error statistics in the node selection process. The method was tested on a binary classification task, detection of impervious surfaces using a Landsat satellite image, and a regression problem, simulation of waveform LiDAR data. In the classification scenario, results indicate that the MSRBF is superior to existing radial basis function and back propagation neural networks in terms of obtained classification accuracy and training-testing consistency, especially for smaller datasets. The latter is especially important as reference data acquisition is always an issue in remote sensing applications. In the regression case, MSRBF provided improved accuracy and consistency when contrasted with a multi kernel RBF network. Results highlight the potential of a novel training methodology that is not restricted to a specific algorithmic type, therefore significantly advancing machine learning algorithms for classification and regression tasks. The MSRBF is expected to find numerous applications within and outside the remote sensing field.